INDEX

Abbasi, Sohaib, 234

Ab Initio Software Corporation, 233

A/B testing, 242

Accenture, 77, 78, 85, 208

AC Milan, 42

ACNielsen, 131

action, designing decision making for, 163

activity-based costing (ABC), 92

actuarial and risk management 103–105

AdSense, 136–137

advertising, digital, 133, 136–137

AIG, 202

airlines, 59, 78, 88–89

Alderson, Sandy, 37–38

alerts, 253

Alexander, Rob, 70–71

algorithmic trading, 85

Amazon

analytical decision making at, 33–34

analytical leadership at, 193–194

analytics-based competition at, 24–25

Analytics Maturity Assessment of, 47

Echo/Alexa, 255

experiments at, 73

pricing optimization at, 141

senior management commitment at, 55, 56

supply chain management at, 149–151

ambition, 58–61

Ambrose, John, 147

American Airlines, 59, 78, 88

American Express, 103, 123–124

analysts

amateur, 208–216, 259

in culture wars, 174–175

data access for, 225–226

data scientists, 5, 6, 13

data scientists vs. quantitative, 5, 204–205

in DELTA model, 17, 46–47, 178, 179–180

DELTA model on, 47

future of, 256–259

managing, 200–208, 258–259

organization of, 202–203

outsourcing, 86, 208

quantitative, 5, 13–14

software as replacement for, 187–190

analytical capabilities

assessing, 160–163

choosing a path for, 164–165

DELTA model on, 177–181

key elements in, 161

managing for outcomes and, 181–185

maturity model on, 61

road map to, 36, 157–186

urban legends about, 187–190

analytical companies, 63–64, 174–176

executives’ roles in, 194–199

analytical competition, 21–44

advantages of, 27–30

analytical companies in, 174–176

analytical decision making in, 33–36

aspirations to, 170–174

assessing the degree of, 61–67

choosing strategic focus, 161–162

data and IT capabilities in, 220–221

definition of, 26

differentiation via, 27–28

in every industry, 25, 29–30

four pillars of, 60

full-steam-ahead vs. prove-it path to, 164–170

future of, 249–264

human element in, 36, 43–44

the leading edge in, 36

at Netflix, 21–24

origins of, 30–33

prerequisites to, 159–165

progress toward, 176–186

prove-it detour toward, 165–170

road map for, 36, 157–186

senior executives and, 190–200

in sports, 37–43

stages of, 61–67

analytical competitors, 176

definition of, 45

enterprise-level approach by, 51–54

external processes in, 130

four pillars of, 60

key attributes of, 45–67

large-scale ambition in, 58–61

senior management commitment at, 54–58, 60–61

strategic, distinctive capability at, 48–51

analytical consulting, 85–87

analytical eras. See four eras of business analytics

analytical maturity model. See DELTA model

analytically impaired organizations, 65–67

analytical techniques

additional technical capabilities, 180–181, 183

for amateurs, 212–213

for customer relationship management, 134–135

for internal processes, 92–94

leaders’ knowledge of, 192–193

for marketing, 134–135

for supply chain management, 148–149

Analytics 1.0–4.0. See four eras of business analytics

analytics

commitment to, 75–77

definition of, 25–27

definitions of terms in, 26

DELTA model for capability building in, 17, 46–47

enterprise-level approach to, 51–54

in government, 81–83 (see also government)

localized, 65

Analytics at Work (Davenport, Harris, and Morison), 17, 46

analytics experts

machine learning and, 10–11

skills for, 13–14

analytics hubs, 54

Analytics Maturity Assessment, 46–47

Apex Systems, 124

Apple, 255

architecture, 217–247

of analytical technology, 217–224

analytical tools/applications and, 236–245

data management, 222, 224–233

data repositories, 223, 234–236

data transformation, 233–234

deployment processes and, 246

at Equifax, 244–245

artificial intelligence, 8–11, 118

artisanal analytics, 10, 11–12

Ascential Software, 233

aspirations, 58–61

analytical, 170–174

competitive, 65

assumptions in quantitative analysis, 34

attribute analysis, 23

autonomous analytics, 8–12, 25, 26, 251

autonomous decision-making applications, 213–214

Bain & Company, 74

Bank of America, 52, 106, 196

Barclays, 56–57, 59, 79

barriers, 88–89

Barton, Richard N., 24

baseball, 37–40, 122, 141, 208

basketball, 41–42

Bayesian inference, 92, 97

Beane, Billy, 37–38

Beech, Roland, 42

behaviors, managing, 181

Belichick, Bill, 41, 123

Bell, David, 145

Beracha, Barry, 55, 192

Betterment, 172

Beyene, Saba, 128

Bezos, Jeff, 33–34, 55, 56, 73, 193–194, 229

Bierhoff, Oliver, 42

big data, 4–6

architecture of, 222–223

definition of, 31

mainstream use of, 6–7

origins of, 31

big data analytics, 32

Big Data @ Work (Davenport), 47

“black box” problem in machine learning, 10–11

Blink (Gladwell), 33

Blockbuster, 21

Bloomberg Technology, 151

Bock, Laszlo, 125, 126–127

Boger, Joshua, 118

Boston Celtics, 42

Boston Red Sox, 38–39, 208–209

brand management, 142–143

Bridgewater Associates, 85

Brougher, Bill, 109

business environments, changing, 99–100

business intelligence, 2–3, 25, 31

business intelligence and advanced analytics, 32

business models

digital industrial, 7

management skills for, 14–15

obsolete, 88

BusinessWeek, 24, 83

buy-in, 57–58

Caesars Entertainment, 24

analytical leadership at, 194

analytical professionals at, 201

analytical strategy at, 161–162

distinctive capabilities at, 29

enterprise-level approach at, 51

external processes analytics at, 132–133

market share at, 59

pricing optimization at, 141

sales creation at, 144–145

senior management commitment at, 55, 56

strategic, distinctive capability of, 48

unique strategy of, 79

Cafarella, Mike, 4

campaign management software, 131–132

Canadian Tire Corporation, 230

capability maturity model, 61

capacity planning, 148

Capital One, 12–13

amateur analysts in, 208

ambition at, 58

analytical decision making at, 34

analytical leadership at, 194

creation of, 69–71

data issues at, 225

duplication of strategy at, 79

information-based strategy at, 29

metrics at, 50–51

model management at, 255–256

outperformance of competitors by, 80

results at, 59

senior management commitment at, 55

subprime customers at, 139

talent management at, 125, 127–128

Capital One Health Care, 143–144

CareSage, 85

Carnegie Tech, 30–31

Catalina, 86

Caterpillar, 116–117

cement business, 29, 152–153

CEMEX, 29, 152–153

CHAID (chi-square automatic interaction detection), 134

change management, 115

Chicago Cubs, 39–40, 194

Chico, 177

chief analytics officers, 53–54

chief data and analytics officers (CDAOs), 53–54, 198–199

chief data officers (CDOs), 53–54

chief financial officers (CFOs), 94–95, 194–196

chief information officers (CIOs), 196–198

Cinematch, 22

Cisco Systems, 12, 96, 257

city planning, 83

Clark, Ben, 73

clickstream data, 4–6

Climate Pro, 7

clinical trial design, 119

closed loops of analytics, 142–143

cloud, the, 9, 35–36, 242–243

Coalition Against Insurance Fraud, 103–104

cognitive technologies, 8–11, 236–237, 240

collaborative filtering analytics, 150–151

Collins, Jim, 73

combinatorial optimization, 92–93

combinatorics, 148

company value, 99

competitive advantage

analytics as, 78–80

internal processes in, 91–92

complacency, 176

CompStat, 81

computer-aided design (CAD), 108–109, 241

configuration problem, 110–111

conflicts of interest, 96–97

conjoint analysis, 134

constraint analysis, 93

consulting, analytical, 85–87

content personalization, 146–147

cost management, 100–103

credit card industry, 69–70, 103–104

credit scores, 49–51

Credit Suisse, 98

crime statistics analysis, 81–82, 83, 163

cross-selling, 137

Cuban, Mark, 43

culture

in Analytics 1.0, 3

in Analytics 2.0, 6

in Analytics 4.0, 10–11

changing, 14–15, 55–56

data-driven, 262–263

of data scientists, 204–205

executive commitment to, 55–56

executives in changing, 190–192, 196–198

at Marriott, 71–72

at Netflix, 23–24

wars, 174–175

customer experience life cycle model, 79

customer loyalty, 59

customer relationship management, 99, 129–147

analytical techniques in, 134–135

attracting/retaining customers, 133–139

brand management and, 142–143

connecting suppliers with customers and, 147–151

content personalization and, 146–147

creating sales and, 143–145

life cycle management and, 145

pricing optimization and, 135, 140–141

Cutting, Doug, 4

cybersecurity, 104–105

dashboards, 99

data

acquisition of, 232

capabilities by analytical competition stage, 220–221

cleansing, 232

in DELTA model, 17, 46–47, 177, 178

dirty, 224–225

getting, cleaning, and loading, 3

governance, 225, 231–232

increasing amounts of, 34–35

labeled training, 255

maintenance of, 232–233

management, 27

organization and storage, 232

pervasive, 250–251

quality, 225, 230–231

quantities, 225, 229–230

relevance of, 225–227

sourcing, 225, 227–229

structuring, 13

transformation, 233–234

value extraction from, 1–2, 230–231

data analytics

Analytics 1.0, 2–4

Analytics 2.0, 4–6

Analytics 3.0, 6–7

Analytics 4.0, 8–12

eras in, 1–17

organizational implications of, 12–15

pace of change in, 1

skills for, 13–14

data cubes, 235

data lakes, 233, 236, 244

data literacy, 211

data management, 33, 222, 224–233

data marts, 31, 235

data mining, 31, 240

data products, 5

data repositories, 223, 234–236

DataRobot, 9

data scientists, 5

culture and attitude of, 204–205

education of, 6

experimentation skills for, 13

future of, 256–259

job requirements for, 204

managing, 200–208

“Data Scientist: The Sexiest Job of the 21st Century” (Davenport and Patil), 5

data visualization, 239–242

data warehousing, 31, 229–230, 234–235

Davenport, Thomas H., 5, 17, 46, 259

decision making

analytical, 33–36

in analytical competition, 28

automating without monitoring, 186

autonomous, 213–214, 251

designing into processes, 163

enterprise systems and, 75

fact-based, 33

at Netflix, 23–24

override issue in, 214–216

performance and, 73–74

decision support, 91

in Analytics 1.0, 3

in Analytics 2.0, 5

in Analytics 3.0, 6–7

external reporting/scorecards and, 96–98

decision support systems (DSS), 30–31

deep learning, 8, 14

Deere & Company, 59

Dell, 97–98, 110

Deloitte, 49, 85, 105, 195, 208, 261

DELTA model, 17, 46–47, 177–183

DELTTA model, 47

demand-supply matching, 148

de Molina, Al, 196

Department of Veterans Affairs, 83

deployment processes, 223, 246

descriptive analytics, 25

in Analytics 1.0, 2–3

definition of, 26

external reporting/scorecards and, 96–98

localized analytics and, 65

tools, 243

See also business intelligence

Dhore, Prasanna, 244

diapers and beer urban legend, 187–188

Dibble, Bill, 104

differentiation, 27–28

digital analytics, 242

digital industrial business model, 7

Disney’s Parks and Resorts, 142–143

Disraeli, Benjamin, 186

distinctive capabilities

of analytical competitors, 45, 48–51

in analytics, 28–29

based on analytics, 21–44

competitive advantage and, 78–80

DnB NOR, 137

Dodd-Frank Act, 223

Domain Awareness System, 81–82

Dow Chemical, 159

Drucker, Peter, 260–261

Dunnhumby, 86, 138

dynamic pricing, 140

Earthgrains, 192

eBay, 122

econometric analysis, 133, 136–137

econometric modeling, 134, 142

edge analytics, 228

Einstein, 9

E. & J. Gallo, 30, 60–61, 194

embedded analytics, 77, 260

employee experience life cycle model, 79

employee learning and innovation, 99

employees

core skills for, 210–211

culture wars between, 174–175

human resource analytics and, 122–128

managing analytical, 187–216

override issue and, 214–216

energy cost management, 101–103

Entelos, Inc., 120

enterprise-level approach, 17, 46–47, 51–54, 177, 178

amateur analysts and, 208–216

to data sourcing, 227–229

enterprise performance management, 98–100

enterprise resource planning (ERP) systems, 30, 31

enterprise systems

analytical tools in, 237

as data source, 227

decision making and, 75

Epstein, Theo, 38, 40

Equifax, 244–245

eras, analytics. See four eras of business analytics

Eskew, Mike, 152

ESPN, 40

Evalueserve, 208

event streaming, 241

event triggers, 137–138, 253

Everest Group, 96–97

executives

analytical, characteristics of, 192–193

CFOs, 94–95, 194–196

commitment of, 54–58, 60–61, 176, 199–200

in culture change, 14–15, 55–56

fact-based decision making and, 160

in full-steam-ahead approach, 164–165

managing analytical people and, 190–200

passion for analytics in, 55–56

roles of, 194–199

sponsorship by, 36

executive support systems, 31

experimental design, 93

experimentation, 73

external processes and, 130

at Google, 109

in marketing, 134

skills in, 13

worker skills in, 210

explanatory analytics, 253

external processes, 129–153

customer-based, 133–147

supply-based, 147–153

extract, transform, and load (ETL), 3, 233–234

eye tracking, 109

Facebook, 5, 6, 9

Fairbank, Rich, 55, 56, 69–70

fashion industry, 29

Fast Company, 73

FedEx, 152

FICO Corporation, 35, 246

FICO scores, 49–51, 80

Field of Dreams approach, 185

financial analytics, 94–105

cost management, 100–103

enterprise performance management/scorecards, 98–100

external reporting, 96–98

risk management, 103–105

financial results, 184–185

financial services

analytical products/services in, 85

Analytics Maturity Assessment of, 47

credit cards, 69–70

metrics development in, 49–51

risk management in, 103–105

senior management commitment in, 56–57

wealth management, 171–174

Fincher, David, 23

Firewire Surfboards, 108, 110

First Analytics, 112–113

fivethirtyeight.com, 39, 40–41

Fleet Bank, 106

flexibility, 12–13, 58, 79

focus, choosing strategic, 162

football, 41, 61, 122–123

four eras of business analytics, 1–15

Analytics 1.0, 2–4

Analytics 2.0, 4–6

Analytics 3.0, 6–7

Analytics 4.0, 8–12

four pillars of analytical competition, 60

Ford, 110–111

Foxwoods Resort Casino, 79

Franks, Bill, 6

fraud

financial services, 103–105

tax, 82, 83

welfare, 82

Friedman, Frank, 195

full-steam-ahead approach, 164–165

funding of analytical groups, 206

future-value analysis, 93

Gallo, Joe, 60–61, 194

Garga, Sunil, 86–87

Garmin TruSwing, 87

Gartner, 32, 258

GE, 7, 84–85

data management at, 225

differentiation at, 29

GE Capital, 208

General Motors, 110–111

genetic algorithms, 93

Genpact, 208

Gibson, William, 249

Gillette, 34, 106

Gladwell, Malcolm, 33

Golden State Warriors, 41–42

golf club sensors, 87, 260

Good to Great (Collins), 73

Google, 5

analytical leadership at, 194

Analytics, 84, 87

analytics-based competition at, 24–25

artificial intelligence software, 262

digital advertising at, 133, 136–137

experimentation at, 109

experiments at, 73

human resource management at, 43

human resources analytics, 122

machine cognition at, 9

PageRank algorithm, 5

People and Innovation Lab (PiLab), 126

Project Oxygen, 126

talent management at, 125–127, 202, 210

governance, 206, 225, 231–232

government

analytics in, 81–83

as data source, 228

Internal Revenue Service (IRS) programs, 82

New York City’s CompStat program, 81

Singapore’s “Smart Nation” program, 83

UK police analytics insights, 163

graphics processing units (GPUs), 9

Green Bay Packers, 41

Griffiths, Hugh, 146

Grove, Jonathan, 103

H2O, 13

Hadoop, 4, 229, 235, 244

Harrah’s Entertainment. See Caesars

Harris, Jeanne G., 17, 46

Hastings, Reed, 21, 22, 24, 55, 56

health care, 82–83

cost management, 101

insurance and credit scores in, 50

research and development analytics, 120–121

Healthways, 121

Henry, John, 38

Hewlett-Packard, 97

Hive, 4

Holland, Chuck, 57

HOLT, 98

Honda, 111–112

Houghton Mifflin Harcourt, 144

House of Cards, 23

Houston Rockets, 42, 43

Hoyer, Jed, 38, 40

human element in analytics, 36, 43–44

change driven by, 256–259

urban legends on, 187–190

See also data scientists; quantitative analysts

human resource information systems (HRIS), 122

human resources analytics, 122–128

human resources management, 187–216

of analytical amateurs, 208–216

of analytical professionals and data scientists, 200–208

executives’ roles in, 194–199

in sports, 40–43

urban legends about, 187–190

Hunt, Neil, 23–24

IBM, 208

analytical tools, 35

Cognos, 239

Global Services, 85

merger and acquisition algorithms, 34, 106

on performance and analytics use, 77

SPSS, 31

Watson, 9

image mining, 254–255

ImageNet, 8, 255

industrial internet, 84–85

Infinity Property & Casualty, 104

Informatica Corporation, 233, 234, 236

information distribution, 221

information management, 27, 206

information orientation, 198

Information Resources Inc., 86–87, 131

information technology

in architecture of analytical technology, 218–224

cybersecurity in, 104–105

industry, 97

signposts of effective, 222–223

vision for, 171

InformationWeek, 70–71, 253

information workers, core skills for, 210–211

initiatives, evaluating, 184

in-memory processing, 252

innovation, 117–121

in silico research, 120

insurance fraud, 103–104

integer programming, 92–93

Intel, 95

Intermountain Healthcare, 121

internal processes, 91–128.

analytical techniques for, 92–94

financial analytics, 94–105

human resources analytics, 122–128

operational analytics, 106–117

mergers and acquisition analytics, 105

research and development analytics, 117–121

See also processes

Internal Revenue Service (IRS), 82

National Research Program, 82

Taxpayer Compliance Measurement Program, 82

International Data Corporation (IDC), 74, 217, 237

International Institute for Analytics, 6, 46–47, 63

Internet of Things, 15, 34–35, 228, 250–251

Intuit, 73

intuitive decision making, 29–30

evidence on, 33

executive commitment and, 199–200

on pricing, 140

inventory optimization, 59

Irish Tax and Customs Authority, 83

Ittner, Chris, 99

Jagex Games Studio, 146

James, Bill, 38–39, 42

James, Brent, 121

JetBlue, 88

Johnson & Johnson, 120

kaizen, 131

Keen, Peter, 30

Kheradpir, Shaygan, 196–197

Kirby, Julia, 259

Kizer, Kenneth W., 83

Klein, Gary, 33

Knight, Phil, 190–191

knowledge discovery, 31

Komatsu, Shigeru, 112

Korn Ferry, 49

Kraft, Bob, 61, 194

Kraft, Jonathan, 56, 61, 194

Kroger, 59, 86, 139

Lane, Katrina, 201

Larcker, David, 99

law enforcement analytics. See government

leadership, 177, 178–179

analytical, characteristics of, 192–193

in building analytics capability, 17, 46–47

in DELTA model, 17, 46–47, 166, 178–179

emergence of analytical, 193–194

See also executives

Leahy, Terry, 138, 139

Lever Brothers, 136

Leverhulme, Lord, 136

Levis, Jack, 57–58, 114–116

Lewis, Michael, 37, 80, 122

Lewis, Peter, 229

Liberson, Dennis, 127

lifetime value analysis, 134

LinkedIn, 5

Little, Grady, 39

localized analytics, 65

location analysis, 148

Lofgren, Chris, 56

logistics analytics, 114–117

logistics management, 151–153

Loop AI Labs, 9

Loveman, Gary, 51, 55, 56, 141, 194

loyalty programs, 72, 86, 138–139

machine learning, 8–11, 14, 240

banking industry, 70–71

models for, 10–11

Macy’s, 140–141

management

alerts, 253

in analytical competition development, 36

by analytical competitors, 45

of analytical people, 187–216, 258–259

commitment of senior, 54–58

enterprise-level approach to analytics and, 51–54

fact-based, 25

managing for outcomes and, 181–185

of models, 255–256

skills across analytics eras, 13–15

management consulting, 49

manufacturing analytics, 106–111

market experiments, 134

marketing

analytical techniques in, 134–135

brand management and, 142–143

price optimization and, 135, 140–141

real-time, 132–133

market share, 59

Marriott, J. Willard, 71

Marriott International, 71–72, 197

data sharing at, 260

distinctive capability at, 51

overrides in, 214–215

strategic, distinctive capability at, 48

Mars, 25

Massachusetts Institute of Technology (MIT), 77, 78

Maynard, Peter, 244, 245

McDonald, Robert, 83, 194, 197, 211

McKinsey Solutions, 49

McNamara, Robert, 81

Medicaid, 82–83

Medicare, 82–83

MediSpend, 97

Meng, Xiao-Li, 192–193

mergers and acquisitions, 34

analytics on, 105–106

meritocracy, 193

metadata repositories, 235

metrics, 261

on analytical activity results, 58–59

exploitation and exploration of, 49–51

at Marriott, 71

monitoring strategic, 163

staffing, 125

Microsoft, 5

analytical capabilities in software by, 35, 251

crime statics analysis, 81–82

energy cost management, 101–103

machine cognition at, 9

Milan Lab, 42

mining, 35

MIT Sloan Management Review, 77

MIT Sloan Sports Analytics Conference, 41, 42

MMIS, Inc., 35

mobile devices, 35, 132–133

Model Factory, 11–12

modeling

predictive, 33, 110–111

propensity, 11–12

supply chains, 148–149

uplift, 135

model management, 255–256

Modern Analytics, 11–12

Modular Category Assortment Planning System, 148

Moneyball (Lewis), 37, 80, 122

Monsanto, 7

Monte Carlo simulation, 93

Moore’s Law, 8

Morey, Daryl, 42, 43

Morison, Robert F., 17, 46

Morris, Nigel, 67, 69–70

multiple regression analysis, 93–94, 135

Mu Sigma, 208, 257

natural disaster preparedness, 83

natural language processing, 14, 241

needs assessment processes, 52

Netflix, Inc., 21–24

analytical leadership at, 194

experiments at, 73

senior management commitment at, 55, 56

strategic, distinctive capability of, 48, 51

neural networks, 8, 94, 245

New England Patriots, 41, 61, 122–123, 194

NewVantage Partners, 14, 262–263

New York Police Department, 81–82

Nike, 190–191

Nucleus Research, 237

numeracy, 210–211

O2, 146

Oakland A’s, 24, 37–38, 49

Oberhelman, Doug, 116

offshoring, 86, 208, 257–258

oil exploration, 35

OLAP (online analytical processing), 31, 238–239

On Assignment, 125

open-source technologies

analyst skills in, 13

analytical tools, 35, 237

for big data processing, 4–5

distributed data frameworks, 235–236

machine learning, 9

for machine learning, 9–11

statistical programming, 26–27

operational analytics, 6, 106–117

logistics, 114–117

manufacturing, 106–111

quality, 111–112

safety, 112–114

operations, 99

Optum, 7

Oracle, 35

ORION. See UPS ORION project

Ormanidou, Elpida, 177

outcomes, managing for, 181–185

outsourcing, 86, 208, 257–258

override issues, 214–216

Owens & Minor, 51

Partners HealthCare, 215

Passerini, Filippo, 197

passion, 192

Patil, D. J., 5, 201

Perez, William, 191

performance, 67, 69–89

analytics as competitive advantage and, 78–80

commitment to analytics and, 75–77

drivers, improving, 162–163

evidence assessment and, 73–78

external reporting/scorecards and, 96–98

managing for outcomes and, 181–185

market for analytical products/services and, 84–87

monitoring, 31

predictions on, 97–98

See also metrics

performance reporting. See descriptive analytics

personalization, 146–147

pharmaceutical industry, 80, 118–120

Philips, 85

pickstreams, 35

Pig, 4

Pioli, Scott, 123

Planck, Max, 15

Podium Data, 236

point-of-sale (POS) systems, 30, 31

politics, 207

predictive analytics, 25, 254

in Analytics 1.0, 2

in customer life cycle management, 145

definition of, 26

at Netflix, 22–23

performance and embedded, 77, 260

predictive lead scoring systems, 143–144

predictive modeling, 33, 130

prescriptive analytics, 25, 254

in Analytics 1.0, 2

definition of, 26

Price, Mark, 108

Price, Paul, 106

price management and profit optimization (PMPO) solutions, 140

price optimization, 135, 140–141

pricing trend analysis, 130

priorities, 177, 185

technology investments and, 219

privacy, customer, 137–138

processes

analytics integration in, 6

deployment, 223, 246

external, 129–153

internal, 91–128

managing for outcomes, 183

Procter & Gamble, 25, 34, 211

analytical leadership at, 194

data scientists at, 202

experiments at, 73

external processes analytics at, 131

mergers and acquisitions, 34, 106

metrics at, 59

outsourcing at, 258

reporting relationships at, 197

productivity, 122

products

constraint analysis for, 93

development of, 5, 6–7

extensions of existing, 117–118

incorporating analytics in, 87

managing for outcomes, 183–184

research and development analytics, 117–121

Profit InSight, 107–108

programs, managing for outcomes, 183

Progressive Insurance, 50–51, 72, 80, 139, 219

Snapshot, 229

propensity modeling, 11–12

prove-it detour, 165–170

Python, 4

QlikView, 239, 243

quality analytics, 24, 106–107, 111–112

quantitative algorithms, 239

quantitative analysis, 33

assumptions in, 34

at Netflix, 24

quantitative analysts, 5, 13–14

Quill, 98

R (programming language), 4, 237

radio frequency identification (RFID) sensors, 34–35

RapidMiner, 237

RBC Financial Group, 52

real-time analytics, 252–253

Recorded Future, 261

regulated industries, 80, 221–222

external reporting in, 96–98

Reinhardt, Anders, 210–211

relational databases, 238–239

Renaissance Technologies, 85

renewable advantage, 80

Renwick, Glenn, 219

reporting, 96–98, 221–222

research and development (R&D) analytics, 117–121

resources

allocation of, 163

commitment of, 165

nonfinancial/intangible, 99

retention agents, 145

return on investment (ROI), 74

revenue management, 71, 135

revenue opportunity, 71

revenue optimization, 82

Ricketts, Tom, 40, 194

right-time analytics, 252–253

risk management, 103–105, 121

Rocky Mountain Steel Mills, 107–108, 110

Roland Rating, 42

routing, 149

Royal Bank of Canada, 52

rule engines, 239

RuneScape, 146

Ruthven, Graham, 42

sabermetrics, 38–39

safety analytics, 112–114

sales, converting customer interactions into, 143–145

Salesforce.com, 9, 35, 143, 251, 254

sales trend analysis, 130

San Antonio Spurs, 41–42

San Diego Padres, 38

San Francisco Giants, 141

SAP, 35, 239

Sara Lee Bakery Group, 55

Sarbanes-Oxley Act of 2002, 221–222

SAS Institute, 9, 31, 35, 242–243, 245

Enterprise Miner, 245

High-Performance Analytics, 244

scenario planning, 142–143

scheduling, 149

Schmidt, Steve, 119–120, 243

Schneider National, 54, 56, 113, 197

scientific retailing, 140

scorecards, 98–100, 261

search engine optimization, 135

self-service analytics, 2–3

services, managing for outcomes, 183–184

shareholders, external reporting to, 96–98

Signet Bank, 58, 69

Silver, Nate, 39, 40–41

Simchi-Levi, David, 77

Simon, Herbert, 30–31

Simon, Rob, 107–108

simulation, 93, 94

supply chain, 149

tools for, 241

Singapore “Smart Nation” initiative, 83

situational awareness systems, 261

Six Sigma, 106, 131

Skillsoft, 147

skimming the cream off the garbage, 139

Smart Inventory Management System (SIMS), 110–111

Smith, Darrell, 101–102

soccer, 42

social media, 15, 228

social media analytics, 242

software, 223

analytical, 26–27, 30, 35

in Analytics 1.0, 3

in Analytics 4.0, 8

capability maturity model for, 61

decision support systems, 30–31

democratization of analytics, 251–252

humans replaced by, 187–190

for pricing, 140–141

Sorrell, Martin, 136

Southwest, 88

Spacey, Kevin, 23

Spark, 4, 235

speech mining, 254–255

sponsors, 36

in analytical aspirations stage, 170–174

in analytical companies, 174

in full-steam-ahead approach, 164–165

in prove-it detour, 166

sports, analytics in, 37–43, 122–123

pricing optimization, 141

strategic, distinctive capability and, 48

spreadsheets, 3, 212–213, 238

error rates in, 53

Sprint, 79, 145, 158

SPSS, 31

Stabell, Charles, 30

staffing metrics, 125

Stanley, Tim, 132

statistical algorithms, 239

statistical analysis, 33

Strata + Hadoop World conference, 146

strategy, 7

analytical competitors and, 45

change driven by, 259–261

choosing focus or target in, 162

cost management and, 100–103

distinctive capabilities in, 48–51

internal processes and, 91–92

management skills for, 14–15

suppliers, sharing data with, 132–133

supply chain management, 129–133, 147–153

analytical techniques in, 148–149

connecting customers and suppliers in, 147–151

logistics analytics in, 116–117

logistics management in, 151–153

optimization in, 6

support vector machine, 135

sustainable pipelines, 205–206

systems, analytics integration in, 6

Tableau, 239, 243

Talend, 233

talent management, 125–126

Tamr, 234

Target, 137–138

targets, 17, 46–47, 178, 179

choosing strategic, 162

tax fraud, 82, 83

Taylor, David, 211

Tesco, 86, 138–139

Tesla, 118

“test and learn” approach, 23–24

text categorization, 241

text mining, 240, 254–255

textual analysis, 94

Thomas, Charles, 54

Thompson, Mike, 112

threat signatures, 104–105

time series experiments, 135

tools and technology, 236–245

for analytical amateurs, 212–213

for analytical competition, 32, 34–36

in Analytics 1.0, 2–3

in Analytics 2.0, 4–5

architecture of, 217–224

change driven by, 250–256

in DELTTA model, 180

machine learning, 8–11

market for analytical, 84–87

performance and spending on, 77

returns from investments in, 74–75

for self-service analytics, 2–3

in sports data, 43

visual analytics, 243

Toshiba Semiconductor Company, 111–112

Total Quality Management, 106

transaction data, 29

transformation tools, 222

transportation safety analytics, 112–113

Trifacta, 234

Trillium, 233

Troutwine Athletic Profile, 123

TrueCar, Inc., 236

trust, 203–204

Two Sigma, 85

uniqueness, 79

United Healthcare, 7

University of Utah Healthcare, 101

uplift modeling, 135

UPS ORION project, 7

change management in, 13

logistics analytics in, 114–116, 151–152

real-time analytics in, 252

savings from, 59

senior management commitment in, 57–58

Value Driven Outcomes (VDO), 101

value drivers, 99

VantageScore, 49–51

Vardi, Nathan, 85

Varian, Hal, 202

VELUX Group, 210–211

Verizon, 196–197

Verizon Wireless, 85

Vertex Pharmaceuticals, 65, 118–120, 243

Veterans Affairs (VA) hospitals, 83

Visa, 47, 103

vision, 171

visual analytics approach, 112, 223, 243

Walmart, 51

human resources analytics, 122

pricing, 140

strategic, distinctive capability of, 48

supply chain management at, 147–149, 260

talent management at, 125, 128

Wanamaker, John, 133, 136

Wayman, Bob, 97

Wealthfront, 172

wealth management, 171–174

web analytics, 242

welfare fraud detection, 82

Wells Fargo, 54, 205

Werner, Tom, 38

wine business, 30, 60–61

WPP plc, 136

Wynn, Steve, 79

Yahoo!, 9, 229

Yankee Group, 140

yield analysis, 94, 112

yield management, 59, 78, 135

YouTube, 255

Zillow, 5

Zimmer Biomet, 97